<\/p>\n
Engineers at MIT (Massachusetts Institute of Technology<\/a>) have developed a technique for forecasting technological advancement. Researchers successfully determined the improvement rates of 28 different technologies, including solar photovoltaic, 3-D printing, fuel-cell technology, and genome sequencing. They started by scanning approximately 500,000 technology related patents using a new method for selecting the patents that suit each technology.\u00a0By analysing metrics of patents in each domain they found that some forward citations predict technological growth more than others. They devised an equation using those metrics and using the equation, figured out the rate of improvement for 28 technologies. They say their method is significantly less laborious than most traditional forecasting techniques.<\/p>\n <\/p>\n Benson and Chris Magee, Professors of the Practice of Engineering Systems at MIT, have published their results in the journal\u00a0PLoS ONE.\u00a0<\/em>The paper contains fundamental findings and equations relating technological improvement to a variety of patent characteristics.\u00a0Chris Magee, who collaborated with Benson, has been working on the forecasting method since 2003. He has since been trying\u00a0to find the rate of technological growth by comparing the data with Moore\u2019s Law.\u00a0In 2010, he realized that one of the best resources on technology lay in the US patent record and decided to utilize the same. The team identified the most relevant patents in a technology domain by literally reading through thousands of patents. In 2012, Magee and Benson came up with the method for identifying relevant patent sets, by choosing patents that overlap between the US and international patent-classification systems.<\/p>\n <\/p>\n The team could repeatedly identify same set of patents that represent a technology, within hours rather than months, by looking for overlap between the US and\u00a0different\u00a0classification systems. From these relevant set of patents, researchers picked out metrics that denoted a technology\u2019s rate of improvement. They found that average forward citations within the first three years after publication and average date of publication were the metrics that predict technological improvement and devised the equation using them.\u00a0Then they used their approach to predict the improvement rates of 11 emerging technologies for the next 10 years. According to the results, the fastest-growing domains appear to be online learning and digital representation, while slower technologies include food engineering and nuclear fusion.<\/p>\n <\/p>\n Magee hopes the method may be used much like a rating system, similar to Standard & Poor\u2019s and other stock-market indices. \u201cWe can help reduce the uncertainty of the capabilities of a technology in the future, not to zero, but to a more manageable number<\/em>,\u201d Benson says. \u201cI believe that\u2019s valuable in a lot of different ways<\/em>.\u201d This method should be useful for any organization willing to invest in technology, especially government agencies and firms that are in the technology business.<\/p>\n <\/p>\n Source : here<\/a><\/p>\n Image Source \/Attribution: here<\/a>\u00a0(Governed by Creative Commons License CC BY SA 2.0<\/a>)<\/p>\n <\/p>\n","protected":false},"excerpt":{"rendered":" MIT researchers have introduced a patent-based method for forecasting technological growth, focusing on citation metrics and classification system overlaps. Their approach provides organisations with a structured, less labor-intensive technique to predict technology improvement rates and reduce investment uncertainty.<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"iawp_total_views":22,"footnotes":""},"categories":[14],"tags":[6573,7125,7126,3023,7127,193,7124,7128],"class_list":["post-21433","post","type-post","status-publish","format-standard","hentry","category-patents","tag-emerging-technologies","tag-innovation-metrics","tag-mit-research","tag-patent-analysis","tag-patent-citations","tag-technological-growth","tag-technology-forecasting","tag-technology-improvement"],"_links":{"self":[{"href":"https:\/\/www.bananaip.com\/intellepedia\/wp-json\/wp\/v2\/posts\/21433","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.bananaip.com\/intellepedia\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.bananaip.com\/intellepedia\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.bananaip.com\/intellepedia\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.bananaip.com\/intellepedia\/wp-json\/wp\/v2\/comments?post=21433"}],"version-history":[{"count":3,"href":"https:\/\/www.bananaip.com\/intellepedia\/wp-json\/wp\/v2\/posts\/21433\/revisions"}],"predecessor-version":[{"id":132816,"href":"https:\/\/www.bananaip.com\/intellepedia\/wp-json\/wp\/v2\/posts\/21433\/revisions\/132816"}],"wp:attachment":[{"href":"https:\/\/www.bananaip.com\/intellepedia\/wp-json\/wp\/v2\/media?parent=21433"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.bananaip.com\/intellepedia\/wp-json\/wp\/v2\/categories?post=21433"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.bananaip.com\/intellepedia\/wp-json\/wp\/v2\/tags?post=21433"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}